Large scale machine learning systems and methods

ABSTRACT

A system for generating a model is provided. The system generates, or selects, candidate conditions and generates, or otherwise obtains, statistics regarding the candidate conditions. The system also forms rules based, at least in part, on the statistics and the candidate conditions and selectively adds the rules to the model.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/736,193, filed Apr. 17, 2007, which is a continuation of U.S. patentapplication Ser. No. 10/734,584, filed Dec. 15, 2003 (now U.S. Pat. No.7,222,127), which is a continuation-in-part of U.S. patent applicationSer. No. 10/706,991, filed Nov. 14, 2003 (now U.S. Pat. No. 7,231,399),the disclosures of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to classification systems and,more particularly, to systems and methods for applying machine learningto various large data sets to generate a classification model.

2. Description of Related Art

Classification models have been used to classify a variety of elements.The classification models are built from a set of training data thatusually includes examples or records, each having multiple attributes orfeatures. The objective of classification is to analyze the trainingdata and develop an accurate model using the features present in thetraining data. The model is then used to classify future data for whichthe classification is unknown. Several classification systems have beenproposed over the years, including systems based on neural networks,statistical models, decision trees, and genetic models.

One problem associated with existing classification systems has to dowith the volume of training data that they are capable of handling.Existing classification systems can only efficiently handle smallquantities of training data. They struggle to deal with large quantitiesof data, such as more than one hundred thousand features.

Accordingly, there is a need for systems and methods that are capable ofgenerating a classification model from a large data set.

SUMMARY OF THE INVENTION

Systems and methods, consistent with the principles of the invention,apply machine learning to large data sets to generate a classificationmodel.

In accordance with one aspect consistent with the principles of theinvention, a system for generating a model is provided. The system mayinclude multiple nodes. At least one of the nodes is configured toselect a candidate condition, request statistics associated with thecandidate condition from other ones of the nodes, receive the requestedstatistics from the other nodes, form a rule based, at least in part, onthe candidate condition and the requested statistics, and selectivelyadd the rule to the model.

According to another aspect, a system for generating a model isprovided. The system may form candidate conditions and generatestatistics associated with the candidate conditions. The system may alsoform rules based, at least in part, on the candidate conditions and thegenerated statistics and selectively add the rules to the model.

According to yet another aspect, a method for generating a model in asystem that includes multiple nodes is provided. The method may includegenerating candidate conditions, distributing the candidate conditionsto the nodes, and generating statistics regarding the candidateconditions. The method may also include collecting the statistics foreach of the candidate conditions at one of the nodes, generating rulesbased, at least in part, on the statistics and the candidate conditions,and selectively adding the rules to the model.

According to a further aspect, a system for generating a model isprovided. The system may generate new conditions and distribute the newconditions to a set of nodes. Each of the nodes may generate statisticsregarding the new conditions. The system may generate new rules based,at least in part, on the statistics and the new conditions and add atleast one of the new rules to the model.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an embodiment of the inventionand, together with the description, explain the invention. In thedrawings,

FIG. 1 is a diagram of an exemplary model generation system according toan implementation consistent with the principles of the invention;

FIG. 2 is an exemplary diagram of a node of FIG. 1 according to animplementation consistent with the principles of the invention;

FIG. 3 is a flowchart of exemplary processing for generating a modelaccording to a first implementation consistent with the principles ofthe invention; and

FIG. 4 is a flowchart of exemplary processing for generating a modelaccording to a second implementation consistent with the principles ofthe invention; and

FIG. 5 is a flowchart of exemplary processing for generating a modelaccording to a third implementation consistent with the principles ofthe invention.

DETAILED DESCRIPTION

The following detailed description of the invention refers to theaccompanying drawings. The same reference numbers in different drawingsmay identify the same or similar elements. Also, the following detaileddescription does not limit the invention.

Systems and methods consistent with the principles of the invention mayapply machine learning to large data sets, such as data sets includingover one hundred thousand features and/or one million instances. Thesystems and methods may be capable of processing a large data set in areasonable amount of time to generate a classification model.

Different models may be generated for use in different contexts. Forexample, in an exemplary e-mail context, a model may be generated toclassify e-mail as either spam or normal (non-spam) e-mail. In anexemplary advertisement context, a model may be generated to estimatethe probability that a user will click on a particular advertisement. Inan exemplary document ranking context, a model may be generated inconnection with a search to estimate the probability that a user willfind a particular search result relevant. Other models may be generatedin other contexts where a large number of data items exist as trainingdata to train the model.

Exemplary Model Generation System

FIG. 1 is an exemplary diagram of a model generation system 100consistent with the principles of the invention. System 100 may includenodes 110-1 through 110-N (collectively referred to as nodes 110)optionally connected to a repository 120 via a network 130. Network 130may include a local area network (LAN), a wide area network (WAN), atelephone network, such as the Public Switched Telephone Network (PSTN),an intranet, the Internet, a memory device, another type of network, ora combination of networks.

Repository 120 may include one or more logical or physical memorydevices that may store a large data set (e.g., potentially over onemillion instances and/or one hundred thousand features) that may beused, as described in more detail below, to create and train a model. Inthe description to follow, the data set will be described in theexemplary e-mail context and, thus, data items relating to e-mail may bedescribed. One of ordinary skill in the art would understand how toextend the description to other contexts.

In the exemplary e-mail context, the data set in repository 120 will becalled “D.” D may include multiple elements “d,” called instances. Eachinstance d may include a set of features “X” and a label “Y.” In oneimplementation, the label Y may be a boolean value (e.g., “spam” or“non-spam”), which may be called y₀ and y₁. In another implementation,the label Y may be a discrete value (e.g., values corresponding tocategories of labels).

A feature X may be an aspect of the domain (e.g., the e-mail domain)that may be useful to determine the label (e.g., “the number ofexclamation points in the message” or “whether the word ‘free’ appearsin the message”). In one implementation, each feature X may include aboolean value (e.g., a value of zero or one based on whether the word“free” appears in the message). In another implementation, each featureX may include a discrete value (e.g., a value based, at least in part,on the number of exclamation points in the message). In yet anotherimplementation, each feature X may include a real value (e.g., the timeof day a message was sent). An instance d may be written as: d=(x₁, x₂,x₃, . . . , x_(m), y), where x_(i) is the value of the i-th featureX_(i) and y is the value of the label.

Repository 120 could potentially store millions of distinct features.For efficiency, an instance d may be encoded using a sparserepresentation: if x_(i) is zero, then its value is not stored for d.For example, assume that X₂ is the feature “does the word ‘free’ appearin the message.” For a particular instance d, if the word “free” doesnot appear in the message, then x₂ is not stored for d.

Nodes 110 may include entities. An entity may be defined as a device,such as a personal computer, a wireless telephone, a personal digitalassistant (PDA), a lap top, or another type of computation orcommunication device, a thread or process running on one of thesedevices, and/or an object executable by one of these device.

Each of nodes 110 may be responsible for a subset of instances. In oneimplementation, nodes 110 obtain their subset of instances fromrepository 120 when needed. In another implementation, each of nodes 110may optionally store a copy of its subset of instances in a local memory115. In this case, nodes 110 may retrieve their copy from repository120. In yet another implementation, each of nodes 110 may store itssubset of instances in local memory 115 and system 100 may include norepository 120.

FIG. 2 is an exemplary diagram of a node 110 according to animplementation consistent with the principles of the invention. Node 110may include a bus 210, a processor 220, a main memory 230, a read onlymemory (ROM) 240, a storage device 250, one or more input devices 260,one or more output devices 270, and a communication interface 280. Bus210 may include one or more conductors that permit communication amongthe components of node 110.

Processor 220 may include any type of conventional processor ormicroprocessor that interprets and executes instructions. Main memory230 may include a random access memory (RAM) or another type of dynamicstorage device that stores information and instructions for execution byprocessor 220. ROM 240 may include a conventional ROM device or anothertype of static storage device that stores static information andinstructions for use by processor 220. Storage device 250 may include amagnetic and/or optical recording medium and its corresponding drive.

Input device(s) 260 may include one or more conventional mechanisms thatpermit an operator to input information to node 110, such as a keyboard,a mouse, a pen, voice recognition and/or biometric mechanisms, etc.Output device(s) 270 may include one or more conventional mechanismsthat output information to the operator, including a display, a printer,a speaker, etc. Communication interface 280 may include anytransceiver-like mechanism that enables node 110 to communicate withother nodes 110 and/or repository 120.

As will be described in detail below, node 110, consistent with theprinciples of the invention, may perform certain operations relating tomodel generation. Node 110 may perform these operations in response toprocessor 220 executing software instructions contained in acomputer-readable medium, such as memory 230. A computer-readable mediummay be defined as one or more physical or logical memory devices and/orcarrier waves.

The software instructions may be read into memory 230 from anothercomputer-readable medium, such as data storage device 250, or fromanother device via communication interface 280. The softwareinstructions contained in memory 230 causes processor 220 to performprocesses that will be described later. Alternatively, hardwiredcircuitry may be used in place of or in combination with softwareinstructions to implement processes consistent with the principles ofthe invention. Thus, implementations consistent with the principles ofthe invention are not limited to any specific combination of hardwarecircuitry and software.

Exemplary Model Generation Processing

To facilitate generation of the model, a prior probability of the labelfor each instance may be determined: P(Y|Z). This prior probability canbe based on Z, which may include one or more values that differ based onthe particular context in which the model is used. Typically, Z may bereal valued and dense (i.e., it does not include many zero entries formany of the instances). In the e-mail context, each e-mail may beevaluated using a common spam detection program that gives each e-mail ascore (e.g., Spam Assassin). The output of the spam detection programmay be used as the prior probability that the e-mail is spam.

A set of instances based on the same or a different set of instances asin repository 120 or memory 115 may be used as “training data” D. Foreach instance d in the training data D, its features (X₀, X₁, . . . ,X_(m)) may be extracted. For example, X₀ may be the featurecorresponding to “the message contains the word ‘free.’” In thisimplementation, the feature X₀ may include a boolean value, such that if“free” appears in the message, then x₀ is one, otherwise x₀ is zero. Inother implementations, the features may include discrete values. It maybe assumed that many of the features will have values of zero.Accordingly, a sparse representation for the features of each instancemay be used. In this case, each instance may store only features thathave non-zero values.

As will be explained later, it may be beneficial to quickly obtainstatistics for the instances that contain particular features. Tofacilitate fast identification of correspondence between features andinstances, a feature-to-instance index may be generated in someimplementations to link features to the instances in which they areincluded. For example, for a given feature X, the set of instances thatcontain that feature may be listed. The list of instances for a featureX is called the “hitlist for feature X.” Thereafter, given a set offeatures X₀, . . . , X_(m), the set of instances that contain thosefeatures can be determined by intersecting the hitlist for each of thefeatures X₀, . . . , X_(m).

A “condition” C is a conjunction of features and possibly theircomplements. For example, a condition that includes two features is:“the message contains the word ‘free’” and “the domain of the sender is“hotmail.com,” and a condition that includes a feature and a complementof a feature is: “the message contains the word ‘free’” and “the domainof the sender is not ‘netscape.net.’” For any instance d_(i), the valueof its features may determine the set of conditions C that apply. A“rule” is a condition C_(i) and a weight w_(i), represented as (C_(i),w_(i)). The model M may include a set of rules and a prior probabilityof the label.

Based, at least in part, on this information, a function may be createdthat maps conditions to a probability of the label: P(Y|C₁, . . . ,C_(n), Z). The posterior probability of the label given a set ofconditions, P(Y|C₁, . . . , C_(n), Z), may be determined using thefunction:Log {P(Y=y ₀ |C ₁ , . . . , C _(n) , Z)/P(Y=y ₁ |C ₁ , . . . , C _(n) ,Z)}=Sum_(i) {−w _(i) I(C _(i))}+Log {P(Y=y ₀ |Z)/P(Y=y ₁ |Z)},  (Eqn. 1)where I(C_(i))=0 if C_(i)=false, and I(C_(i))=1 if C_(i)=true.

Thereafter, given a new instance d and a model M, the posteriorprobability of the label may be determined by: (1) extracting thefeatures from the instance, (2) determining which rules apply, and (3)combining the weight of each rule with the prior probability forinstance d. Therefore, the goal is to generate a good model. To generatea good model, the following information may be beneficial: the set ofconditions C₁, . . . , C_(n), and the values of weights w₁, . . . ,w_(n).

FIG. 3 is a flowchart of exemplary processing for generating a modelaccording to a first implementation consistent with the principles ofthe invention. This processing may be performed by a combination ofnodes 110. Each node 110 may include a copy of the model M and a subsetof instances with a current probability of Y=y₁ for each instance. Eachnode 110 may build its own feature-to-instance index for its subset ofinstances.

Processing may begin with an empty model M that includes the priorprobability of the label. A node 110 may select a candidate condition Cto be tested (act 310). It may be possible for multiple nodes 110, orall of nodes 110, to concurrently select candidate conditions. In oneimplementation, nodes 110 may select candidate conditions from theinstances in training data D. For example, for each instance,combinations of features that are present in that instance (orcomplements of these features) may be chosen as candidate conditions. Inanother implementation, random sets of conditions may be selected ascandidate conditions. In yet another implementation, single featureconditions may be considered as candidate conditions. In a furtherimplementation, existing conditions in the model M may be augmented byadding extra features and these augmented conditions may be consideredas candidate conditions. In yet other implementations, candidateconditions may be selected in other ways.

Node 110 may then estimate a weight w for condition C (act 320). Assumethat condition C includes three features: X₁ and X₅ and X₁₀. In order tofind the set of instances that satisfy condition C, node 110 may use itsfeature-to-instance index. Given the set of instances that satisfy thecondition C, node 110 may gather statistics regarding these instances.If the label of instance d is y[d] and instance d satisfies conditionsC₁, . . . , C_(k), then node 110 may determine first and secondderivatives of:Sum_(d){Log P(Y=y[d]|C ₁ , . . . , C _(k) , C)}−Sum_(d){Log P(Y=y[d]|C ₁, . . . , C _(k))}=Sum_(d){Log P(Y=y[d]|C ₁ , . . . , C _(k) , C)−LogP(Y=y[d]|C ₁ , . . . , C _(k))},  (Eqn. 2)where P(y[d]|C₁, . . . , C_(k), C) is given above (in Eqn. 1) and theweights given above (in Eqn. 1) are the weights in our current model Mtogether with an initial guess for weight w for condition C (or thecurrent weight w for condition C if condition C is already in themodel). Node 110 may then use the derivatives to find an estimatedweight w in a conventional manner using a technique, such as Newton'smethod. Alternatively, weight w for condition C may be estimated using arandom guess, rather than Newton's method.

Node 110 may then generate a request for statistics that node 110 maysend to the other nodes 110 (act 330). The request, in this case, mayinclude the list of features that condition C contains, an identifiercorresponding to node 110, and the estimate of the weight determined bynode 110. Node 110 may broadcast this request to the other nodes 110.

Each of nodes 110 receiving the request (hereinafter “receiving nodes”)may generate statistics for instances that satisfy condition C (act340). For example, a receiving node may use its feature-to-instanceindex to identify the set of instances (within its subset of instancesfor which it is responsible) that correspond to the features ofcondition C. Using this set of instances and the current probability ofY=y₁ for each of these instances, the receiving node may generatestatistics (e.g., derivatives), as described above with respect to Eqn.2. The receiving nodes may then send the statistics to node 110 thatsent the request.

Node 110 may collect statistics from the receiving nodes and use thesestatistics to determine a better weight w for condition C (acts 350 and360). For example, node 110 may use Newton's method to determine a newweight w′ from the derivatives generated by the receiving nodes. Node110 may then use this weight w′ to form a rule or update an existingrule: (C, w′) (act 370).

Node 110 may selectively add the rule to the model M (e.g., add a newrule or update an existing rule in the model M) (act 380). To determinewhether to add the rule, node 110 may compare the likelihood of thetraining data D between the current model with the rule (C, w′) and thecurrent model without the rule (i.e., P(D|M, (C, w′)) vs. P(D|M)). IfP(D|M, (C, w′)) is sufficiently greater than P(D|M), then the rule (C,w′) may be added to the model M. A penalty or “Cost” for each conditionC may be used to aid in the determination of whether P(D|M, (C, w′)) issufficiently greater than P(D|M). For example, if condition C includesmany features, or if the features of condition C are quite rare (e.g.,“does the word ‘mahogany’ appear in the message”), then the cost ofcondition C could be high. The rule (C, w′) may then be added to themodel M if: Log {P(D|M, (C, w′)}−Log {P(D|M)}>Cost(C). If P(D|M, (C,w′)) is not sufficiently greater than P(D|M), then the rule (C, w′) maybe discarded (i.e., not added to the model M), possibly by changing itsweight to zero.

Node 110 may send the rule to the other nodes 110 (e.g., the receivingnodes) (act 390). If node 110 determined that the rule should not beadded to the model M, then node 110 may set the weight for the rule tozero and transmit it to the receiving nodes. Alternatively, node 110 maynot send the rule at all when the rule is not added to the model or therule's weight has not changed. The receiving nodes may use the rule toupdate their copy of the model, as necessary, and update the currentprobabilities of Y=y₁ for the instances that satisfy the conditioncontained in the rule (i.e., condition C). The receiving nodes mayidentify these instances using their feature-to-instance indexes.

Processing may then return to act 310, where node 110 selects the nextcandidate condition. Processing may continue for a predetermined numberof iterations or until all candidate conditions have been considered.During this processing, each condition may eventually be selected onlyonce or, alternatively, conditions may eventually be selected multipletimes.

As described previously, the acts described with respect to FIG. 3 mayoccur on multiple nodes 110 concurrently. In other words, various nodes110 may be sending out statistics requests and processing requests atthe same time. It is not necessary, however, that each of nodes 110perform all of the acts described with regard to FIG. 3. For example, asubset of nodes 110 may select candidate conditions and form rules forthe model. The remaining nodes 110 may process the statistics requests,but form no rules.

FIG. 4 is a flowchart of exemplary processing for generating a modelaccording to a second implementation consistent with the principles ofthe invention. This processing may also be performed by a combination ofnodes 110. Each node 110 may include a copy of the model M and a subsetof instances with a current probability of Y=y₁ for each instance. Eachnode 110 may build its own feature-to-instance index for its subset ofinstances.

Processing may begin with an empty model M that includes the priorprobability of the label. A node 110 may select a candidate condition Cto be tested (act 410). It may be possible for multiple nodes 110, orall of nodes 110, to concurrently select candidate conditions. Candidateconditions may be selected in a manner similar to that described abovewith regard to FIG. 3.

Node 110 may then generate a request for statistics that node 110 maysend to the other nodes 110 (act 420). The request, in this case, mayinclude the list of features that condition C contains and an identifiercorresponding to node 110. Node 110 may broadcast this request to theother nodes 110.

Each of nodes 110 receiving the request (hereinafter “receiving nodes”)may generate statistics for instances that satisfy condition C (act430). For example, a receiving node may use its feature-to-instanceindex to identify the set of instances (within its subset of instancesfor which it is responsible) that correspond to the features ofcondition C. The receiving node may create a histogram of Log P(Y=y₀|C₁,. . . , C_(k)) for the different instances d that satisfy condition Cand are labeled y₀, and create another histogram of Log P(Y=y₁|C₁, . . ., C_(k)) for the different instances d that satisfy condition C and arelabeled y₁. The receiving nodes may then send the statistics to node 110that sent the request.

Node 110 may collect statistics from the receiving nodes and use thesestatistics to determine a weight w for condition C (acts 440 and 450).For example, node 110 may determine an estimate of weight w from:Sum_(d){Log P(Y=y[d]|C₁, . . . , C_(k), C)}. Node 110 may then continueto estimate the weight w (e.g., using a binary search, a hill climbingsearch, or a Newton iteration) until Sum_(d){Log P(Y=y[d]|C₁, . . . ,C_(k), C)} is maximized. Node 110 may then use this weight w to form arule or update an existing rule: (C, w) (act 460).

Node 110 may selectively add the rule to the model M (e.g., add a newrule or update an existing rule in the model M) (act 470). To determinewhether to add the rule, node 110 may compare the likelihood of thetraining data D between the current model with the rule (C, w) and thecurrent model without the rule (i.e., P(D|M, (C, w)) vs. P(D|M)). IfP(D|M, (C, w)) is sufficiently greater than P(D|M), then the rule (C, w)may be added to the model M. As described above, a penalty or “Cost” maybe associated with each condition C to aid in the determination ofwhether P(D|M, (C, w)) is sufficiently greater than P(D|M). If P(D|M,(C, w)) is not sufficiently greater than P(D|M), then the rule (C, w)may be discarded (i.e., not added to the model M), possibly by changingits weight to zero.

Node 110 may send the rule to the other nodes 110 (e.g., the receivingnodes) (act 480). If node 110 determined that the rule should not beadded to the model M, then node 110 may set the weight for the rule tozero and transmit it to the receiving nodes. Alternatively, node 110 maynot send the rule at all when the rule is not added to the model or therule's weight has not changed. The receiving nodes may use the rule toupdate their copy of the model, as necessary, and update the currentprobabilities of Y=y₁ for the instances that satisfy the conditioncontained in the rule (i.e., condition C). The receiving nodes mayidentify these instances using their feature-to-instance indexes.

Processing may then return to act 410, where node 110 selects the nextcandidate condition. Processing may continue for a predetermined numberof iterations or until all candidate conditions have been considered.During this processing, each condition may eventually be selected onlyonce or, alternatively, conditions may be selected multiple times.

As described previously, the acts described with respect to FIG. 4 mayoccur on multiple nodes 110 concurrently. In other words, various nodes110 may be sending out statistics requests and processing requests atthe same time. It is not necessary, however, that each of nodes 110perform all of the acts described with regard to FIG. 4. For example, asubset of nodes 110 may select candidate conditions and form rules forthe model. The remaining nodes 110 may process the statistics requests,but form no rules.

FIG. 5 is a flowchart of exemplary processing for generating a modelaccording to a third implementation consistent with the principles ofthe invention. This processing may also be performed by a combination ofnodes 110. Each node 110 may include a copy of the model M (or afraction of the model M) and a subset of instances with a currentprobability of Y=y₁ for each instance. In this implementation, nodes 110do not use a feature-to-instance index.

Generally, the processing of FIG. 5 may be divided into iterations.Rules may be tested or have their weight optimized once per iteration.Each iteration may be broken into two phases: a candidate rulegeneration phase and a rule testing and optimization phase. The ruletesting and optimization phase may determine the weights for conditionsgenerated in the candidate rule generation phase, and accepts rules intothe model if their benefit (e.g., difference in log likelihood) exceedstheir cost.

Processing may begin with the generation of new conditions as candidateconditions to test whether they would make good rules for the model M(act 510). The generation of new conditions may concurrently occur onmultiple nodes 110. There are several possible ways of generatingcandidate conditions. For example, candidate conditions might includeall conditions with one feature, all conditions with two features thatco-occur in some instance, and all extensions of existing rules by onefeature (where the combination is in some instance). As a furtheroptimization, extensions of only those rules added in the last iterationmay be used.

The goal of the candidate rule generation phase is to generate newconditions that match some minimum number of instances. There are acouple of strategies for accomplishing this. For example, conditionsthat appear multiple times in some fraction of the instances (dividedamong all of nodes 110 and then summed) may be considered. In this case,each node 110 may count the number of instances (of the subset ofinstances for which node 110 is responsible) that match the conditionand generate (condition, count) pairs. The (condition, count) pairs maybe gathered at some node 110 (which may be determined by a rule, such asa hash of the condition) and summed. Conditions with some minimum countvalue may then be kept as candidate conditions. All other conditions maybe dropped.

Alternatively, conditions that appear a certain number of times on asingle node 110 may be considered. In other words, each node 110 maycount the number of instances (of the subset of instances for which node110 is responsible) that match the condition. Conditions with someminimum count value on a single node 110 may be kept as candidateconditions. The candidate conditions may be gathered at some node 110 tofacilitate the removal of duplicate conditions.

Then in the rule testing and optimization phase, the candidateconditions may be distributed to all nodes 110 (act 520). Each node 110may analyze its share of instances to identify which of the candidateconditions match each instance (act 530). Node 110 may store thematching conditions and instances as (condition, instance number) pairs(act 530). Each node 110 may then sort the (condition, instance number)pairs by condition to form a sorted condition-instance list. From thesorted condition-instance list, all instances that match a particularcondition may easily be determined.

Each node 110 may generate statistics for each of the conditions in thesorted condition-instance list (act 540). For example, a node 110 maycollect information regarding predicted label probability from thematching instances and the actual number of observed y₀ labels. In oneexemplary implementation, nodes 110 may build a histogram based, atleast in part, on the collected information and use the histogram as thestatistics relating to the condition. In another exemplaryimplementation, the statistics may take a different form.

Each node 110 may then send the statistics relating to the condition toa particular node 110 designated to handle that condition. Theparticular node 110 may be determined, for example, based on a rule,such as a hash of the condition. Node 110 may collect the statisticsrelating to the condition from the other nodes 110 (act 550). Node 110may then determine an optimal weight w for the rule (C, w) and determinewhether to add the rule to the model M (acts 560 and 570). Node 110 mayuse techniques similar to those described above with regard to FIGS. 3and 4 to determine the optimal weight w and determine whether to add therule to the model M.

Node 110 may then send the rule to the other nodes 110, or just thosenodes 110 that sent statistics (i.e., those nodes 110 with instancesthat match the condition of the rule) (act 580). If node 110 determinedthat the rule should not be added to the model M, then node 110 may setthe weight for the rule to zero and transmit it to the other nodes 110.Alternatively, node 110 may not send the rule at all when the rule isnot added to the model. Nodes 110 that receive the rule may use the ruleto update their copy of the model, as necessary, and update thepredicted label probabilities for the instances that satisfy thecondition contained in the rule.

The rule testing and optimization phase may continue for a number ofiterations or until all rules have been tested. The output of the ruletesting and optimization phase is new weights for all existing rules(possibly zero if the rule is to be dropped from the model M) and a listof new rules.

As described previously, the acts described with respect to FIG. 5 mayoccur on multiple nodes 110 concurrently. In other words, various nodes110 may be concurrently selecting candidate conditions and/or testingrules for the model M. It is not necessary, however, that each of nodes110 perform all of the acts described with regard to FIG. 5. Forexample, a subset of nodes 110 may be responsible for selectingcandidate conditions and/or testing rules for the model.

CONCLUSION

Systems and methods consistent with the principles of the invention maygenerate a model from a large data set (e.g., a data set that includespossibly millions of data items) efficiently on multiple nodes.

The foregoing description of preferred embodiments of the presentinvention provides illustration and description, but is not intended tobe exhaustive or to limit the invention to the precise form disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the invention. Forexample, while series of acts have been described with regard to FIGS.3-5, the order of the acts may be modified in other implementationsconsistent with the principles of the invention. Also, non-dependentacts may be performed in parallel. Further, the acts may be modified inother ways. For example, in another exemplary implementation, acts330-360 of FIG. 3 or acts 420-450 of FIG. 4 may be performed in a loopfor a number of iterations to settle on a good weight.

Also, in the three implementations described with regard to FIGS. 3-5,for each instance d, there is no need to compute the probability of y[d]given model M every time a condition that instance d satisfies istested. Instead, there could be an array that keeps the currentprobability of instance d being y₀ given the model M, and when acondition C is updated, the probabilities for the instances that matchthat condition C may be updated. The probabilities for the instancesthat do not match the condition C may be left unchanged.

It will also be apparent to one of ordinary skill in the art thataspects of the invention, as described above, may be implemented in manydifferent forms of software, firmware, and hardware in theimplementations illustrated in the figures. The actual software code orspecialized control hardware used to implement aspects consistent withthe present invention is not limiting of the present invention. Thus,the operation and behavior of the aspects were described withoutreference to the specific software code—it being understood that one ofordinary skill in the art would be able to design software and controlhardware to implement the aspects based on the description herein.

1. A computer-implemented method, comprising: generating, by one or moreserver devices, a model that is based on: a condition that includes aconjunction of a plurality of features associated with documents, andstatistics, received from one or more other devices, associated with thecondition, where a particular statistic from a particular device, of theone or more other devices, indicates a weight, determined by theparticular device, for the condition, generating the model including:generating a new weight for the condition, for use in the model, basedon the received statistics from the one or more other devices, the newweight for the condition indicating how relevant the condition is, withrespect to other conditions in the model, in determining how relevant adocument is to a search query, generating a candidate rule for the modelbased on the condition and the received statistics, determining whetherto add the candidate rule to the model, and upon determining that thecandidate rule should not be added to the model, setting a weight, forthe rule, to a value that indicates that the candidate rule should notbe added to the model; and using, by the one or more server devices, themodel to generate a rank for a document, the rank indicating anestimated probability that the document is relevant to a received searchquery.
 2. The computer-implemented method of claim 1, where thecondition includes three or more features.
 3. The computer-implementedmethod of claim 1, where a particular statistic received from aparticular device of the one or more other devices indicates a set ofinstances that are associated with the particular device and thatcorrespond to the features of the condition.
 4. The computer-implementedmethod of claim 1, where generating the model includes: replacing, inthe model, a previous rule, that is based on the condition and aprevious weight, with a new rule that is based on the condition and thenew weight.
 5. The computer-implemented method of claim 4, where thereplacing includes: selectively replacing the previous rule with the newrule, based on a cost associated with the condition.
 6. Thecomputer-implemented method of claim 1, where generating the modelincludes: generating a new rule that is based on the condition and thenew weight, and adding the new rule to the model.
 7. Thecomputer-implemented method of claim 6, where the adding is performedselectively, based on a cost associated with the condition.
 8. Thecomputer-implemented method of claim 1, further comprising: generating arule based on the condition and the received statistics, and sending therule to the one or more other devices.
 9. The computer-implementedmethod of claim 1, where the value is zero.
 10. The computer-implementedmethod of claim 1, where generating the model includes: generating acandidate rule for the model based on the condition and the receivedstatistics, determining whether to add the candidate rule to the model,and upon determining that the candidate rule should not be added to themodel, informing the one or more other devices that the candidate ruleshould not be added to the model.
 11. A system comprising: a memory tostore computer-readable instructions; and one or more processors toexecute the instructions to: generate a model that is based on acondition that includes a conjunction of a plurality of featuresassociated with documents, and statistics, received from one or moreother devices, associated with the condition, where a particularstatistic from a particular device, of the one or more other devices,indicates a weight, determined by the particular device, for thecondition, the processor, when generating of the model being further to:generate a new weight for the condition, for use in the model, based onthe received statistics from the one or more other devices, the newweight for the condition indicating how relevant the condition is, withrespect to other conditions in the model, in determining how relevant adocument is to a search query, and generate a candidate rule for themodel based on the condition and the received statistics, determinewhether to add the candidate rule to the model, and upon determiningthat the candidate rule should not be added to the model, set a weight,for the rule, to a value that indicates that the candidate rule shouldnot be added to the model; and use the model to generate a rank for adocument, the rank indicating an estimated probability that the documentis relevant to a received search query.
 12. The system of claim 11,where a particular statistic received from a particular device of theone or more other devices indicates a set of instances that isassociated with the particular device and that correspond to a pluralityof features of the condition.
 13. The system of claim 11, where, whengenerating the model, the one or more processors are to: generate a newrule that is based on the condition and the new weight, and add the newrule to the model.
 14. A memory device comprising: one or moreinstructions which, when executed by a computer device, cause thecomputer device to generate a model that is based on a condition thatincludes a conjunction of a plurality of features associated withdocuments, and statistics, received from one or more other devices,associated with the condition, where a particular statistic from aparticular device, of the one or more other devices, indicates a weight,determined by the particular device, for the condition, the one or moreinstructions to generate the model including: one or more instructionsto generate a new weight for the condition, for use in the model, basedon the received statistics from the one or more other devices, the newweight for the condition indicating how relevant the condition is, withrespect to other conditions in the model, in determining how relevant adocument is to a search query, one or more instructions to generate acandidate rule for the model based on the condition and the receivedstatistics, one or more instructions to determine whether to add thecandidate rule to the model, and one or more instructions to set, upondetermining that the candidate rule should not be added to the model, aweight, for the rule, to a value that indicates that the candidate ruleshould not be added to the model; and one or more instructions which,when executed by the computer device, cause the computer device to usethe model to generate a rank for a document, the rank indicating anestimated probability that the document is relevant to a received searchquery.
 15. The memory device of claim 14, where one or more instructionsto generate the model includes: one or more instructions to replace, inthe model, a previous rule, that is based on the condition and aprevious weight, with a new rule that is based on the condition and thenew weight, and where the one or more instructions to replace theprevious rule include: one or more instructions to selectively replacethe previous rule with the new rule, based on a cost associated with thecondition.
 16. The memory device of claim 14, where the one or moreinstructions to generate the model includes: one or more instructions togenerate a new rule that is based on the condition and the new weight;and one or more instructions to add the new rule to the mode, where theone or more instructions to add the new rule includes: one or moreinstructions to add the new rule selectively, based on a cost associatedwith the condition.
 17. The memory device of claim 14, furthercomprising: one or more instructions to generate a rule based on thecondition and the received statistics; and one or more instructions tosend the rule to the one or more other devices.
 18. The memory device ofclaim 14, where the one or more instructions to generate the modelinclude: one or more instructions to generate a candidate rule for themodel based on the condition and the received statistics; one or moreinstructions to determine whether to add the candidate rule to themodel; and one or more instructions to inform, upon determining that thecandidate rule should not be added to the model, the one or more otherdevices that the candidate rule should not be added to the model.